PURPOSES : This study is to initiated to estimate the impact of mixed traffic flow on expressway section according to the market penetration rate(MPR) of automated vehicles(AVs) using a enhanced intelligent driver model(EIDM). METHODS : To this end, microscopic traffic simulation and EIDM were used to implement mixed traffic flow on basic expressway section and simulation network was calibrated to understand the change of impact in mixed traffic flow due to the MPR of AVs. Additionally, MOEs of mobility aspects such as average speed and travel time were extracted and analyzed. RESULTS : The result of the impact of mobility MOEs by MPR and level of service indicated that 100% MPR of AVs normally affect positive impact on expressway at all level of service. However, it was analyzed that improvements in the level of service from LOS A to C are minimal until the MPR of AVs reaches 75% or higher. CONCLUSIONS : This research shows that impact of MPR of AVs using EIDM of mixed traffic flow on basic expressway. Increasing MPR of AVs affects positive impact on expressway at all level of services. However, MPR from 25% to 75% of AVs in LOS A to C shows minimal impacts. Therefore, to maximize the effectiveness of AVs, appropriate traffic operation and management strategies are necessary.

PURPOSES : The primary objective of this study is to analyze the relationship between the factors that affect traffic incident duration in the mainline, tunnel, and ramp segments of an expressway. In addition, this study derived the most suitable statistical prediction model based on various incident duration distributions. METHODS : South Korean expressway crash data for 11 years, from 2011 to 2021, were analyzed. The incident durations on the mainline, tunnel, and ramp segments were selected using the accelerated failure time model, which is a parametric survival analysis approach. RESULTS : The mainline segment showed that the incident duration increased during accidents, including guard pipe collisions, multivehicle collisions, and snowfall. In particular, collisions in a tunnel with shoulder facilities increase the incident duration, while decreasing the time in the ramp segment. CONCLUSIONS : The incident duration model for each segment type yielded the most accurate results when applying a log-logistic distribution.

PURPOSES : A highway operates in a continuous flow and has restricted access. When an accident occurs on a highway, the impact on the traffic flow is large. In particular, an accident that occurs in a tunnel has a more significant impact than an accident that occurs in a general section. Accordingly, the management agency classifies the tunnel as a dangerous section and manages a tunnel of more than 1000 m using the Tunnel Transportation Management System. The purpose of this study was to select dangerous tunnels that require intensive management for the efficient management of highway tunnels. METHODS : In this study, for the selection of dangerous tunnels for expressways, all highway tunnels were classified into five clusters by characteristics. The traffic accident severity — equivalent property damage only (EPDO) — for each tunnel cluster was derived through a traffic accident analysis. Based on the severity analysis results, the safety performance function (SPF) for each cluster was established, and the accident risk tunnel was selected based on the potential safety improvement (PSI) value of each tunnel calculated using the empirical Bayes (EB) method for each tunnel cluster. RESULTS : As a result of the analysis, accident risk tunnels were selected based on the PSI values of the tunnels for each highway tunnel group. Finally, 55 hazardous tunnels were identified as hazardous tunnels: 13 tunnels in Cluster 1, 3 tunnels in Cluster 2, 15 tunnels in Cluster 3, 18 tunnels in Cluster 4, and 6 tunnels in Cluster 5. CONCLUSIONS : After classifying all 1232 tunnels on the highway into five clusters according to tunnel characteristics, EPDO analysis was performed for each tunnel cluster. To this end, the SPF for each cluster was constructed, and accident risk tunnels were selected based on the PSI value of each tunnel calculated using the EB method for each tunnel cluster. The tunnel cluster was classified as a typical tunnel type. As a result, most of the first and second values were calculated from cluster E (long tunnel cluster).

PURPOSES : In this study, we quantitatively prove the rubber necking phenomenon for highway traffic accidents and develop a calculation model based on the influencing factors. METHODS : Vehicle detector speed data in the opposite direction to the accident point were used based on the accident data on highways over the past three years, and a comparative verification was performed between nearby vehicle detector data to verify the reliability of the data. Accordingly, a binomial logistic model, ordinal probit regression model, and multilinear regression model were developed to compare the orientation. RESULTS : There was a difference in the influencing factors based on the dependent variable, and the day of the week, vehicle type, weather, longitudinal slope, and median height had an effect. Through a regression analysis, an influence coefficient was derived to calculate the driving speed deceleration value by rubbernecking. The results of the model analysis proved that the speed reduction caused by rubbernecking was more evident during the daytime than at night, during weekends compared to weekdays, and the speed reduction was more obvious for heavy vehicles compared to other types of vehicles. It can also be concluded that longer clearance time, higher accident severity, and higher traffic volume affect traffic delay. To verify the data and model equation, the mean prediction bias (MPB) and mean absolute deviation (MAD) were calculated for hundred cases randomly extracted from the collected accident data. These results were excellent. CONCLUSIONS : It can be developed into a human-engineered model that reflects various road/facility conditions, such as highways, other lanes, general roads, and roads without a median strip. This study is meaningful as a basic study on the quantitative effect of rubber necking.

OBJECTIVES: This study aims to review a plan to reduce the shoulder width of a deformed round-trip two-lane highway with low traffic volume. METHODS: Installation of a passing lane on a round-trip two-lane (one-way one-lane) highway, and reduction of a shoulder for a round-trip four-lane highway. RESULTS : It is necessary to establish a design criterion for various highways, because the plan to reduce the lane or shoulder width of a highway with low traffic volume was analyzed to have an economic efficiency of 6.8~7.0%. CONCLUSIONS: It is necessary to seek for a plan to establish a national trunk net early by efficiently using the limited financial resources to cope with the traffic demand elastically.

PURPOSES: This study was initiated to estimate expressway traffic congestion costs by using Vehicle Detection System (VDS) data. METHODS : The overall methodology for estimating expressway traffic congestion costs is based on the methodology used in a study conducted by a study team from the Korea Transport Institute (KOTI). However, this study uses VDS data, including conzone speeds and volumes, instead of the volume delay function for estimating travel times. RESULTS : The expressway traffic congestion costs estimated in this study are generally lower than those observed in KOTI's method. The expressway lines that ranked highest for traffic congestion costs are the Seoul Ring Expressway, Gyeongbu Expressway, and the Youngdong Expressway. Those lines account for 64.54% of the entire expressway traffic congestion costs. In addition, this study estimates the daily traffic congestion costs. The traffic congestion cost on Saturdays is the highest. CONCLUSIONS : This study can be thought of as a new trial to estimate expressway traffic congestion costs by using actual traffic data collected from an entire expressway system in order to overcome the limitations of associated studies. In the future, the methodology for estimating traffic congestion cost is expected to be improved by utilizing associated big-data gathered from other ITS facilities and car navigation systems.

PURPOSES : This study estimated the load effect of a single heavy truck to develop a live load model for the design and assessment of bridges located on an expressway with a limited truck entry weight. METHODS: The statistical estimation methods for the live load effect acting on a bridge by a heavy vehicle are reviewed, and applications using the actual measurement data for trucks traveling on an expressway are presented. The weight estimation of a single vehicle and its effect on a bridge are fundamental elements in the construction of a live load model. Two statistical estimation methods for the application of extrapolation in a probabilistic study and an additional estimation method that adopts the extreme value theory are reviewed. RESULTS : The proposed methods are applied to the traffic data measured on an expressway. All of the estimation methods yield similar results using the data measured when the weight limit has been relatively well observed because of the rigid enforcement of the weight regulation. On the other hand, when the estimations are made using overweight traffic data, the resulting values differ with the estimation method. CONCLUSIONS: The estimation methods based on the extreme distribution theory and the modified procedure presented in this paper can yield reasonable values for the maximum weight of a single truck, which can be applied in both the design and evaluation of a bridge on an expressway.

PURPOSES: The purpose of this study is to analyze the service life of expressway pavement based on both traffic volumes and use of deicing chemicals. METHODS: A database was built using expressway rehabilitation history information from over the last decade. In order to estimate the service life of expressway pavement, various analysis methods were considered, and a decision was made to perform analysis using a method based on an accumulated rehabilitation ratio. The service life of expressway pavement was then analyzed by classifying the scale of traffic volume and extent of de-icing chemicals used. RESULTS: The service life of PMA and SMA ranged from 7.8 to 10.6 years and from 9.9 to 12.0 years, respectively. The service life of JCP ranged from 16.0 to 22.2 years, and the service life of CRCP was 33.5 years on average. Results of assessing service life according to traffic volumes and de-icing chemicals showed that the lower the traffic volumes were, the greater the service life of PMA and JCP, and the less that de-icing chemicals were applied, the greater the service life of JCP. CONCLUSIONS : The dependence of expressway pavement service life on traffic volumes and de-icing chemicals makes it possible to apply LCCA for regional maintenance plans and cost-effective selection of expressway pavement type.

PURPOSES: This study analyzes the available working time at work-zone on the Expressway in accordance to the new capacity manual. METHODS: Sensitivity analysis on variables were conducted to calculate the adjusted capacity at work-zone based on previous researches. RESULTS : The main factors which affect available working time at the work-zone were its capacity, number of lanes, terrain and lane width. Other factors have minimal effect on the available working time. Based on the analysis, a calendar of lane closures was suggested. CONCLUSIONS : A series of studies concluded that the capacity at work-zone in the new capacity manual reduced to 76-82% of the existing manual. As such, the available working time decreased. Furthermore, the factors affecting the available working time needs to be considered when making a plan to rehabilitate the distressed pavement.

PURPOSES : This study is to investigate the relationship of socioeconomic characteristics and road network structure with traffic growth patterns. The findings is to be used to tweak traffic forecast provided by traditional four step process using relevant socioeconomic and road network data. METHODS: Comprehensive statistical analysis is used to identify key explanatory variables using historical observations on traffic forecast, actual traffic counts and surrounding environments. Based on statistical results, a multiple regression model is developed to predict the effects of socioeconomic and road network attributes on traffic growth patterns. The validation of the proposed model is also performed using a different set of historical data. RESULTS : The statistical analysis results indicate that several socioeconomic characteristics and road network structure cleary affect the tendency of over- and under-estimation of road traffics. Among them, land use is a key factor which is revealed by a factor that traffic forecast for urban road tends to be under-estimated while rural road traffic prediction is generally over-estimated. The model application suggests that tweaking the traffic forecast using the proposed model can reduce the discrepancies between the predicted and actual traffic counts from 30.4% to 21.9%. CONCLUSIONS : Prediction of road traffic growth patterns based on surrounding socioeconomic and road network attributes can help develop the optimal strategy of road construction plan by enhancing reliability of traffic forecast as well as tendency of traffic growth.

PURPOSES: Using the collected data for crash, traffic volume, and design elements on ramps between 2007 and 2009, this research effort was initiated to develop traffic crash prediction models for expressway ramps. METHODS: Three negative binomial regression models and three zero-inflated negative binomial regression models were developed for individual ramp types, including direct, semi-direct and loop, respectively. For validating the developed models, authors compared the estimated crash frequencies with actual crash frequencies of twelve randomly selected interchanges, the ramps of which have not been used for model developing. RESULTS: The results show that the negative binomial regression models for direct, semi-direct and loop ramps showed 60.3%, 63.8% and 48.7% error rates on average whereas the zero-inflated negative binomial regression models showed 82.1%, 120.4% and 57.3%, respectively. CONCLUSIONS: Conclusively, the negative binomial regression models worked better in traffic crash prediction than the zero-inflated negative binomial regression models for estimating the frequency of traffic accidents on expressway ramps.

PURPOSES: Because expressway ramps are very complex segments where diverse roadway design elements dynamically change within relatively short length, drivers on ramps are required to drive their cars carefully for safety. Especially, ramps on expressways are designed to guarantee driving at high speed so that the risk and severity of traffic accidents on expressway ramps may be higher and more deadly than other facilities on expressways. Safe deceleration maneuvers are required on off-ramps, whereas safe acceleration maneuvers are necessary on onramps. This difference in required maneuvers may contribute to dissimilar patterns and severity of traffic accidents by ramp types. Therefore, this study was aimed at developing prediction models of the severity of traffic accidents on expressway on- and off-ramps separately in order to consider dissimilar patterns and severity of traffic accidents according to types of ramps. METHODS: Four-year-long traffic accident data between 2007 and 2010 were utilized to distinguish contributing design elements in conjunction with AADT and ramp length. The prediction models were built using the negative binomial regression model consisting of the severity of traffic accident as a dependent variable and contributing design elements as in independent variables. RESULTS: The developed regression models were evaluated using the traffic accident data of the ramps which was not used in building the models by comparing actual and estimated severity of traffic accidents. Conclusively, the average prediction error rates of on-ramps and offramps were 30.5% and 30.8% respectively. CONCLUSIONS: The prediction models for the severity of traffic accidents on expressway on- and off-ramps will be useful in enhancing the safety on expressway ramps as well as developing design guidelines for expressway ramps.

오르막차로는 오르막 구간에서 속도 감소가 큰 대형차의 혼입률이 증가하여 교통용량의 감소가 크게 예상되는 경우, 고속 교통류에서 저속 교통류를 분리하기 위해서 설치된다. 오르막차로와 관련된 기존의 연구는 오르막차로의 구간선정, 시종점 위치, 설치방법 등 주로 도로설계기준과 관련된 내용에 집중되어 왔다. 그러나, 교통운영측면에서 오르막차로는 교통량이 증가할 경우 주요한 병목지점이 된다고 알려져 있는 바, 본 연구에서는 교통운영측면에서 교통량(v/c), 중차량비, 종단경사에 따라 오르막차로 일시 폐쇄가 혼잡 감소에 미치는 영향을 파악하고 아울러 적절한 교통운영기준을 수립하고자 하였다. 중부내륙고속도로 낙동분기점 부근의 오르막구간(136.9K~133.3K, 길이 3.6km, 구배 3.7%)을 대상구간으로 선정하여 교통운영변수에 따른 시뮬레이션 분석을 수행한 결과, 교통량과 중차량비가 오르막차로의 주요한 교통운영기준이 되는 것으로 나타났다. 즉, 오르막차로로 인한 혼잡을 막기 위해서는 교통량비가 0.8이고 중차량비 50%일 때 종단경사와 상관없이 폐쇄하는 것이 효과적이며, 교통량비 1.0일 경우에는 중차량비, 종단경사와 상관없이 오르막차로 폐쇄가 효과적으로 나타났다. 특히 일반적인 소통상황에서는 오르막차로 운영효과가 더 큰 것으로 나타나, 교통량 및 중차량비의 변화에 따라 오르막차로의 탄력적 운영이 필요함을 알 수 있었다. 본 연구를 계기로 도로시설의 탄력적 운영기준에 대한 연구가 활성화될 수 있을 것으로 기대된다.

강우, 강설, 안개발생 등 기상상태의 변화는 운전자의 주행환경에 영향을 미치는 기상요인으로 기상악화시 차량의 차두간격과 속도에 영향을 미치게 되어 도로용량을 감소시키고, 교통사고 발생으로 인한 차로감소 등의 상황을 유발하여 맑은 날보다 더 큰 혼잡을 발생시키는 것으로 분석되었다. 운전자의 시정을 감소시키는 기상악화는 통행속도가 높은 고속도로가 일반도로 보다 기상상태에 따른 통행속도 변화 민감도와 교통사고 심각도가 높게 나타나는 특성이 있다고 분석됨에 따라 고속도로의 교통류 특성변화에는 시정거리가 중요하게 작용하는 것으로 판단되었다. 따라서 본 연구에서는 통행속도가 높은 고속도로 기본구간의 교통류 특성에 영향을 미치는 주요 요인으로 교통량과 속도를 선정하였으며, 일정수준 이상의 교통량 확보가 가능한 수도권내 고속도로를 분석대상으로 선정하고 기상자료와 교통자료를 수집하여 시정거리 변화에 따른 고속도로 교통류 특성변화에 관한 연구를 수행하였다. 본 연구의 수행을 위하여 기존 문헌 고찰을 통해 자료수집 및 분석방법을 수립하고 고속도로의 시정거리 수준을 선정하며, 통계적 차이 검증을 수행하고 시정거리에 따른 고속도로의 교통류 특성 변화를 분석하여 용량 및 서비스 수준 분석 시 적용할 수 있는 방안을 강구하고자 한다.